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I am trying to implement multi-label classification using TensorFlow (i.e., each output pattern can have many active units). The problem has imbalanced classes (i.e., much more zeros than ones in the labels distribution, which makes label patterns very sparse).

The best way to tackle the problem should be to use the tf.nn.weighted_cross_entropy_with_logits function. However, I get this runtime error:

ValueError: Tensor conversion requested dtype uint8 for Tensor with dtype float32

I can't understand what is wrong here. As input to the loss function, I pass the labels tensor, the logits tensor, and the positive class weight, which is a constant:

positive_class_weight = 10
loss = tf.nn.weighted_cross_entropy_with_logits(targets=labels, logits=logits, pos_weight=positive_class_weight)

Any hints about how to solve this? If I just pass the same labels and logits tensors to the tf.losses.sigmoid_cross_entropy loss function, everything works well (in the sense that Tensorflow runs properly, but of course following training predictions are always zero).

See related problem here.

Maxim
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Alberto
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1 Answers1

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The error is likely to be thrown after the loss function, because the only significant difference between tf.losses.sigmoid_cross_entropy and tf.nn.weighted_cross_entropy_with_logits is the shape of the returned tensor.

Take a look at this example:

logits = tf.linspace(-3., 5., 10)
labels = tf.fill([10,], 1.)

positive_class_weight = 10
weighted_loss = tf.nn.weighted_cross_entropy_with_logits(targets=labels, logits=logits, pos_weight=positive_class_weight)
print(weighted_loss.shape)

sigmoid_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=labels, logits=logits)
print(sigmoid_loss.shape)

Tensors logits and labels are kind of artificial and both have shape (10,). But it's important that weighted_loss and sigmoid_loss are different. Here's the output:

(10,)
()

This is because tf.losses.sigmoid_cross_entropy performs reduction (the sum by default). So in order to replicate it, you have to wrap the weighted loss with tf.reduce_sum(...).

If this doesn't help, make sure that labels tensor has type float32. This bug is very easy to make, e.g., the following declaration won't work:

labels = tf.fill([10,], 1)  # the type is not float!

You might be also interested to read this question.

Maxim
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  • Thanks so much, this solved the problem. I had to both explicitly cast the labels into `float32`, and to reduce the loss output using `tf.reduce_sum(...)`. – Alberto Dec 18 '17 at 14:40